Differential Expression - Normal lung versus lung with distal tumor
2018-02-05
library(DESeq2)
library(bcbioRNASeq)
# Shared R Markdown settings
prepareRNASeqTemplate()
if (file.exists("setup.R")) {
source("setup.R")
}
# Directory paths
dataDir <- file.path(params$outputDir, "data")
countsDir <- file.path(params$outputDir, "results", "counts")
deDir <- file.path(params$outputDir, "results", "differential_expression")
# Load bcbioRNASeq object
bcbName <- load(params$bcbFile)
bcb <- get(bcbName, inherits = FALSE)
# subset to second prep batch as all tumors were done in second batch
mycolData <- colData(bcb) %>% as.data.frame()
bcb_sub <- bcb_sub[,which(mycolData$sampleclass %in% c("primary_tumor", "lung_notumor"))]
mycolData <- colData(bcb_sub) %>% as.data.frame()
bcb_sub <- bcb_sub[,which(mycolData$prepBatch==2)]
# drop low RIN value
mycolData <- colData(bcb_sub) %>% as.data.frame()
mycolData$numericrin <- as.numeric(as.character(mycolData$rin))
bcb_sub <- bcb_sub[,which(mycolData$numericrin>3)]
bcb <- bcb_sub
bcb_sub_lungnotumor_tumor <- bcb
saveData(bcb_sub_lungnotumor_tumor, dir=dataDir)Overview
- Principal Investigator:
- Researcher:
- Experiment:
> dds <- bcbio(bcb, "DESeqDataSet")
> design(dds) <- params$design
> dds <- DESeq(dds)
> rld <- rlog(dds)Alpha level (FDR) cutoffs
Let’s take a look at the number of genes we get with different false discovery rate (FDR) cutoffs. These tests subset P values that have been multiple test corrected using the Benjamini Hochberg (BH) method (Benjamini and Hochberg 1995).
> alphaSummary(dds)| 0.1 | 0.05 | 0.01 | 0.001 | 1e-06 | |
|---|---|---|---|---|---|
| LFC > 0 (up) | 4994, 25% | 4604, 23% | 3884, 19% | 3195, 16% | 1990, 9.9% |
| LFC < 0 (down) | 5375, 27% | 4870, 24% | 4088, 20% | 3335, 17% | 2185, 11% |
| outliers | 64, 0.32% | 64, 0.32% | 64, 0.32% | 64, 0.32% | 64, 0.32% |
| low counts | 1926, 9.6% | 2312, 11% | 2697, 13% | 2697, 13% | 4238, 21% |
| cutoff | (mean count < 1) | (mean count < 1) | (mean count < 1) | (mean count < 1) | (mean count < 4) |
Results
> # help("results", "DESeq2")
> # For contrast argument as character vector:
> # 1. Design matrix factor of interest.
> # 2. Numerator for LFC (expt).
> # 3. Denominator for LFC (control).
> resUnshrunken <- results(
+ dds,
+ contrast = params$contrast,
+ alpha = params$alpha)
>
> # DESeqResults with shrunken log2 fold changes (LFC)
> # help("lfcShrink", "DESeq2")
> # Only `coef` or `contrast` can be specified, not both
> # Use the correct `coef` number to modify from `resultsNames(dds)`
> resShrunken <- lfcShrink(
+ dds = dds,
+ # coef = 2,
+ contrast = params$contrast,
+ res = resUnshrunken)
>
> # Use shrunken LFC values by default
> res <- resShrunken
> res_lungnotumor_tumor <- res
> saveData(res_lungnotumor_tumor, dir = dataDir)We performed the analysis using a BH adjusted P value cutoff of 0.05 and a log fold-change (LFC) ratio cutoff of 1.
Plots
Mean average (MA)
An MA plot compares transformed counts on M (log ratio) and A (mean average) scales (Y. H. Yang et al. 2002).
> plotMA(res)
Volcano
A volcano plot compares significance (BH-adjusted P value) against fold change (log2) (Cui and Churchill 2003; Li et al. 2014). Genes in the green box with text labels have an adjusted P value are likely to be the top candidate genes of interest.
> plotVolcano(res, lfc = params$lfc)
Heatmap
This plot shows only differentially expressed genes on a per-sample basis. We have scaled the data by row and used the ward.D2 method for clustering (Ward 1963).
> plotDEGHeatmap(res, counts = rld)
> top50res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:50, ]
> top50gene <- row.names(top50res)
>
> plotHeatmap(bcb, top50gene, normalized = "rlog")
File downloads
The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.
Count matrices
normalizedCounts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.rawCounts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.
Differentially expressed genes (DEG)
> resTbl <- resultsTables(res, lfc = params$lfc, write = TRUE, summary = TRUE,
+ headerLevel = 3, dir = deDir)Summary statistics
- 25168 genes in count matrix
- base mean > 0: 20137 genes (non-zero)
- base mean > 1: 17848 genes
- alpha cutoff: 0.05
- lfc cutoff: 1 (applied in tables only)
- deg pass alpha: 9474 genes
- deg lfc up: 3039 genes
- deg lfc down: 3710 genes
DEG tables are sorted by BH-adjusted P value, and contain the following columns:
ensgene: Ensembl gene identifier.baseMean: Mean of the normalized counts per gene for all samples.log2FoldChange: log2 fold change.lfcSE: log2 standard error.stat: Wald statistic.pvalue: Walt test P value.padj: BH adjusted Wald test P value (corrected for multiple comparisons; aka FDR).externalGeneName: Ensembl name (a.k.a. symbol).description: Ensembl description.geneBiotype: Ensembl biotype (e.g.protein_coding).
Top tables
Only the top up- and down-regulated genes (arranged by log2 fold change) are shown.
> topTables(resTbl)| ensgene | baseMean | lfc | padj | symbol | description |
|---|---|---|---|---|---|
| ENSMUSG00000026822 | 4823 | 5.67 | 0.00e+00 | Lcn2 | lipocalin 2 |
| ENSMUSG00000058773 | 1635 | 5.57 | 5.65e-148 | Hist1h1b | histone cluster 1, H1b |
| ENSMUSG00000105827 | 2827 | 4.29 | 1.97e-139 | Hist2h2bb | histone cluster 2, H2bb |
| ENSMUSG00000017716 | 1271 | 6.79 | 2.28e-139 | Birc5 | baculoviral IAP repeat-containing 5 |
| ENSMUSG00000041782 | 520 | 5.36 | 1.73e-137 | Lad1 | ladinin |
| ENSMUSG00000001622 | 1568 | 9.22 | 1.30e-136 | Csn3 | casein kappa |
| ENSMUSG00000029304 | 8499 | 4.57 | 1.52e-133 | Spp1 | secreted phosphoprotein 1 |
| ENSMUSG00000033256 | 452 | 4.27 | 4.74e-116 | Shf | Src homology 2 domain containing F |
| ENSMUSG00000035105 | 721 | 7.33 | 3.66e-113 | Egln3 | egl-9 family hypoxia-inducible factor 3 |
| ENSMUSG00000000567 | 406 | 6.75 | 4.38e-112 | Sox9 | SRY (sex determining region Y)-box 9 |
| ENSMUSG00000028645 | 901 | 4.34 | 1.07e-111 | Slc2a1 | solute carrier family 2 (facilitated glucose transporter), member 1 |
| ENSMUSG00000026185 | 9195 | 4.09 | 3.09e-111 | Igfbp5 | insulin-like growth factor binding protein 5 |
| ENSMUSG00000059040 | 1894 | 3.62 | 4.41e-111 | Eno1b | enolase 1B, retrotransposed |
| ENSMUSG00000038400 | 1684 | 3.25 | 1.06e-109 | Pmepa1 | prostate transmembrane protein, androgen induced 1 |
| ENSMUSG00000049539 | 1508 | 5.75 | 6.28e-106 | Hist1h1a | histone cluster 1, H1a |
| ENSMUSG00000069308 | 498 | 4.33 | 1.29e-102 | Hist1h2bp | histone cluster 1, H2bp |
| ENSMUSG00000052565 | 3121 | 3.97 | 8.09e-93 | Hist1h1d | histone cluster 1, H1d |
| ENSMUSG00000038679 | 1843 | 3.80 | 9.64e-86 | Trps1 | transcriptional repressor GATA binding 1 |
| ENSMUSG00000069268 | 545 | 4.50 | 3.02e-85 | Hist1h2bf | histone cluster 1, H2bf |
| ENSMUSG00000069266 | 551 | 4.02 | 1.57e-84 | Hist1h4b | histone cluster 1, H4b |
| ENSMUSG00000026051 | 1164 | 9.50 | 1.14e-83 | 1500015O10Rik | RIKEN cDNA 1500015O10 gene |
| ENSMUSG00000024640 | 749 | 4.99 | 4.48e-81 | Psat1 | phosphoserine aminotransferase 1 |
| ENSMUSG00000075031 | 1266 | 3.94 | 5.16e-80 | Hist1h2bb | histone cluster 1, H2bb |
| ENSMUSG00000044303 | 365 | 5.01 | 4.00e-78 | Cdkn2a | cyclin-dependent kinase inhibitor 2A |
| ENSMUSG00000017390 | 713 | 5.22 | 4.84e-78 | Aldoc | aldolase C, fructose-bisphosphate |
| ENSMUSG00000064288 | 4484 | 3.34 | 1.92e-77 | Hist1h4k | histone cluster 1, H4k |
| ENSMUSG00000009185 | 441 | 8.17 | 5.92e-76 | Ccl8 | chemokine (C-C motif) ligand 8 |
| ENSMUSG00000027469 | 392 | 4.74 | 4.42e-73 | Tpx2 | TPX2, microtubule-associated |
| ENSMUSG00000069301 | 264 | 4.88 | 4.72e-73 | Hist1h2ag | histone cluster 1, H2ag |
| ENSMUSG00000101972 | 1524 | 4.48 | 5.96e-72 | Hist1h3i | histone cluster 1, H3i |
| ENSMUSG00000033006 | 307 | 7.47 | 9.61e-72 | Sox10 | SRY (sex determining region Y)-box 10 |
| ENSMUSG00000047945 | 830 | 3.71 | 2.08e-71 | Marcksl1 | MARCKS-like 1 |
| ENSMUSG00000074480 | 231 | 5.04 | 8.12e-71 | Mex3a | mex3 RNA binding family member A |
| ENSMUSG00000054342 | 460 | 4.29 | 1.97e-70 | Kcnn4 | potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4 |
| ENSMUSG00000023885 | 247 | 5.22 | 5.90e-70 | Thbs2 | thrombospondin 2 |
| ENSMUSG00000070803 | 350 | 7.47 | 1.09e-69 | Cited4 | Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 4 |
| ENSMUSG00000012428 | 401 | 4.32 | 5.64e-69 | Steap4 | STEAP family member 4 |
| ENSMUSG00000063021 | 546 | 4.60 | 1.36e-67 | Hist1h2ak | histone cluster 1, H2ak |
| ENSMUSG00000022382 | 215 | 4.28 | 1.90e-66 | Wnt7b | wingless-type MMTV integration site family, member 7B |
| ENSMUSG00000036777 | 233 | 3.86 | 1.90e-66 | Anln | anillin, actin binding protein |
| ENSMUSG00000020737 | 1939 | 3.75 | 3.20e-66 | Jpt1 | Jupiter microtubule associated homolog 1 |
| ENSMUSG00000041431 | 201 | 5.29 | 5.04e-66 | Ccnb1 | cyclin B1 |
| ENSMUSG00000062727 | 916 | 4.62 | 3.35e-65 | Hist1h2bk | histone cluster 1, H2bk |
| ENSMUSG00000025574 | 844 | 3.69 | 2.44e-64 | Tk1 | thymidine kinase 1 |
| ENSMUSG00000025747 | 594 | 3.99 | 3.91e-64 | Tyms | thymidylate synthase |
| ENSMUSG00000037035 | 374 | 3.04 | 4.95e-64 | Inhbb | inhibin beta-B |
| ENSMUSG00000101355 | 2216 | 4.74 | 1.07e-63 | Hist1h3h | histone cluster 1, H3h |
| ENSMUSG00000074403 | 2315 | 4.55 | 1.37e-63 | Hist2h3b | histone cluster 2, H3b |
| ENSMUSG00000081058 | 1164 | 5.19 | 1.33e-62 | Hist2h3c2 | histone cluster 2, H3c2 |
| ENSMUSG00000006403 | 205 | 6.63 | 2.90e-62 | Adamts4 | a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondin type 1 motif, 4 |
| ensgene | baseMean | lfc | padj | symbol | description |
|---|---|---|---|---|---|
| ENSMUSG00000023959 | 4864 | -8.58 | 0.00e+00 | Clic5 | chloride intracellular channel 5 |
| ENSMUSG00000029188 | 7690 | -8.72 | 0.00e+00 | Slc34a2 | solute carrier family 34 (sodium phosphate), member 2 |
| ENSMUSG00000056492 | 5611 | -5.85 | 7.50e-272 | Adgrf5 | adhesion G protein-coupled receptor F5 |
| ENSMUSG00000015452 | 5684 | -8.65 | 1.23e-260 | Ager | advanced glycosylation end product-specific receptor |
| ENSMUSG00000054619 | 3126 | -6.55 | 1.55e-254 | Mettl7a1 | methyltransferase like 7A1 |
| ENSMUSG00000024140 | 12792 | -5.89 | 1.75e-251 | Epas1 | endothelial PAS domain protein 1 |
| ENSMUSG00000021789 | 8511 | -9.90 | 5.71e-234 | Sftpa1 | surfactant associated protein A1 |
| ENSMUSG00000068874 | 1625 | -6.01 | 2.94e-223 | Selenbp1 | selenium binding protein 1 |
| ENSMUSG00000025150 | 10555 | -6.65 | 5.08e-212 | Cbr2 | carbonyl reductase 2 |
| ENSMUSG00000003477 | 6041 | -10.06 | 2.60e-210 | Inmt | indolethylamine N-methyltransferase |
| ENSMUSG00000053198 | 1486 | -5.91 | 9.18e-208 | Prx | periaxin |
| ENSMUSG00000045954 | 2272 | -4.17 | 3.05e-191 | Cavin2 | caveolae associated 2 |
| ENSMUSG00000021057 | 2868 | -8.60 | 1.70e-184 | Akap5 | A kinase (PRKA) anchor protein 5 |
| ENSMUSG00000041134 | 1019 | -5.42 | 3.34e-175 | Cyyr1 | cysteine and tyrosine-rich protein 1 |
| ENSMUSG00000049690 | 1202 | -4.79 | 2.10e-174 | Nckap5 | NCK-associated protein 5 |
| ENSMUSG00000074743 | 2910 | -6.77 | 3.40e-171 | Thbd | thrombomodulin |
| ENSMUSG00000029375 | 2890 | -8.83 | 2.22e-169 | Cxcl15 | chemokine (C-X-C motif) ligand 15 |
| ENSMUSG00000014846 | 1540 | -5.89 | 2.82e-167 | Tppp3 | tubulin polymerization-promoting protein family member 3 |
| ENSMUSG00000046733 | 1142 | -6.11 | 2.38e-165 | Gprc5a | G protein-coupled receptor, family C, group 5, member A |
| ENSMUSG00000004655 | 2579 | -4.85 | 2.90e-163 | Aqp1 | aquaporin 1 |
| ENSMUSG00000028713 | 1267 | -6.78 | 9.61e-162 | Cyp4b1 | cytochrome P450, family 4, subfamily b, polypeptide 1 |
| ENSMUSG00000045930 | 1282 | -5.96 | 7.24e-161 | Clec14a | C-type lectin domain family 14, member a |
| ENSMUSG00000032473 | 3985 | -9.93 | 2.28e-159 | Cldn18 | claudin 18 |
| ENSMUSG00000033032 | 1152 | -4.93 | 9.53e-158 | Afap1l1 | actin filament associated protein 1-like 1 |
| ENSMUSG00000052974 | 3771 | -8.25 | 3.16e-157 | Cyp2f2 | cytochrome P450, family 2, subfamily f, polypeptide 2 |
| ENSMUSG00000033960 | 2890 | -4.00 | 1.12e-153 | Jcad | junctional cadherin 5 associated |
| ENSMUSG00000022836 | 1476 | -4.57 | 3.29e-150 | Mylk | myosin, light polypeptide kinase |
| ENSMUSG00000020154 | 4103 | -5.88 | 1.53e-146 | Ptprb | protein tyrosine phosphatase, receptor type, B |
| ENSMUSG00000020315 | 15138 | -3.29 | 1.64e-145 | Sptbn1 | spectrin beta, non-erythrocytic 1 |
| ENSMUSG00000041378 | 1240 | -6.37 | 9.49e-144 | Cldn5 | claudin 5 |
| ENSMUSG00000000093 | 913 | -6.35 | 1.10e-143 | Tbx2 | T-box 2 |
| ENSMUSG00000054986 | 3630 | -10.17 | 2.15e-140 | Sec14l3 | SEC14-like lipid binding 3 |
| ENSMUSG00000001240 | 1114 | -5.61 | 2.39e-140 | Ramp2 | receptor (calcitonin) activity modifying protein 2 |
| ENSMUSG00000017754 | 2088 | -3.61 | 2.91e-140 | Pltp | phospholipid transfer protein |
| ENSMUSG00000006386 | 1282 | -5.76 | 9.80e-140 | Tek | endothelial-specific receptor tyrosine kinase |
| ENSMUSG00000015354 | 1275 | -5.55 | 1.21e-139 | Pcolce2 | procollagen C-endopeptidase enhancer 2 |
| ENSMUSG00000030020 | 776 | -6.21 | 2.08e-139 | Prickle2 | prickle planar cell polarity protein 2 |
| ENSMUSG00000030340 | 1275 | -7.62 | 4.08e-136 | Scnn1a | sodium channel, nonvoltage-gated 1 alpha |
| ENSMUSG00000024451 | 771 | -4.44 | 1.34e-135 | Arap3 | ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 3 |
| ENSMUSG00000069763 | 1552 | -8.39 | 2.72e-133 | Tmem100 | transmembrane protein 100 |
| ENSMUSG00000048960 | 1789 | -5.80 | 2.82e-133 | Prex2 | phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2 |
| ENSMUSG00000018339 | 2143 | -4.20 | 9.28e-132 | Gpx3 | glutathione peroxidase 3 |
| ENSMUSG00000020681 | 3749 | -4.59 | 3.87e-131 | Ace | angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 |
| ENSMUSG00000020044 | 3960 | -4.29 | 1.03e-130 | Timp3 | tissue inhibitor of metalloproteinase 3 |
| ENSMUSG00000069515 | 7947 | -7.54 | 1.72e-130 | Lyz1 | lysozyme 1 |
| ENSMUSG00000021904 | 935 | -6.85 | 1.77e-128 | Sema3g | sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3G |
| ENSMUSG00000001300 | 1523 | -4.24 | 3.27e-125 | Efnb2 | ephrin B2 |
| ENSMUSG00000002980 | 2215 | -6.34 | 8.21e-125 | Bcam | basal cell adhesion molecule |
| ENSMUSG00000053279 | 1924 | -9.09 | 3.36e-122 | Aldh1a1 | aldehyde dehydrogenase family 1, subfamily A1 |
| ENSMUSG00000042812 | 1273 | -8.18 | 1.10e-121 | Foxf1 | forkhead box F1 |
Expression patterns
> top10res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:10, ]
> top10gene <- row.names(top10res)
>
> plots <- plotGene(bcb, top10gene, returnList = TRUE)
>
> n = 1
> cat(paste("##", plots[[n]]$labels$title))Clic5
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Lcn2
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Slc34a2
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Adgrf5
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Ager
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Mettl7a1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Epas1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Sftpa1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Selenbp1
> plots[[n]]
> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))Cbr2
> plots[[n]]
Methods
RNA-seq counts were generated by bcbio and bcbioRNASeq using salmon (Patro et al. 2017). Counts were imported into R using tximport (Soneson, Love, and Robinson 2016) and DESeq2 (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015).
R session information
> mdHeader("`devtools::session_info()`", level = 2)devtools::session_info()
> devtools::session_info()## setting value
## version R version 3.4.3 (2017-11-30)
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
## date 2018-02-05
##
## package * version date
## acepack 1.4.1 2016-10-29
## affy 1.56.0 2017-10-31
## affyio 1.48.0 2017-10-31
## annotate 1.56.1 2017-11-13
## AnnotationDbi 1.40.0 2017-10-31
## AnnotationFilter 1.2.0 2017-10-31
## AnnotationHub 2.10.1 2017-11-08
## assertthat 0.2.0 2017-04-11
## backports 1.1.2 2017-12-13
## base * 3.4.3 2017-12-07
## base64enc 0.1-3 2015-07-28
## basejump 0.2.0 2018-01-29
## bcbioBase * 0.0.3 2018-01-29
## bcbioRNASeq * 0.1.4 2018-01-30
## bindr 0.1 2016-11-13
## bindrcpp * 0.2 2017-06-17
## Biobase * 2.38.0 2017-10-31
## BiocGenerics * 0.24.0 2017-10-31
## BiocInstaller 1.28.0 2017-10-31
## BiocParallel 1.12.0 2017-10-31
## biomaRt 2.34.2 2018-01-20
## Biostrings 2.46.0 2017-10-31
## bit 1.1-12 2014-04-09
## bit64 0.9-7 2017-05-08
## bitops 1.0-6 2013-08-17
## blob 1.1.0 2017-06-17
## broom 0.4.3 2017-11-20
## cellranger 1.1.0 2016-07-27
## checkmate 1.8.5 2017-10-24
## circlize 0.4.3 2017-12-20
## cli 1.0.0 2017-11-05
## cluster 2.0.6 2017-03-10
## codetools 0.2-15 2016-10-05
## colorspace 1.3-2 2016-12-14
## compiler 3.4.3 2017-12-07
## ComplexHeatmap 1.17.1 2017-10-25
## ConsensusClusterPlus 1.42.0 2017-10-31
## cowplot 0.9.2 2017-12-17
## crayon 1.3.4 2017-09-16
## curl 3.1 2017-12-12
## data.table 1.10.4-3 2017-10-27
## datasets * 3.4.3 2017-12-07
## DBI 0.7 2017-06-18
## DEGreport * 1.14.1 2017-12-19
## DelayedArray * 0.4.1 2017-11-07
## dendsort 0.3.3 2015-12-14
## DESeq2 * 1.18.1 2017-11-12
## devtools 1.13.4 2017-11-09
## digest 0.6.15 2018-01-28
## dplyr * 0.7.4 2017-09-28
## edgeR 3.20.7 2018-01-18
## ensembldb 2.2.0 2017-10-31
## evaluate 0.10.1 2017-06-24
## forcats * 0.2.0 2017-01-23
## foreign 0.8-69 2017-06-22
## formatR 1.5 2017-04-25
## Formula 1.2-2 2017-07-10
## genefilter 1.60.0 2017-10-31
## geneplotter 1.56.0 2017-10-31
## GenomeInfoDb * 1.14.0 2017-10-31
## GenomeInfoDbData 1.0.0 2018-01-29
## GenomicAlignments 1.14.1 2017-11-18
## GenomicFeatures 1.30.2 2018-01-31
## GenomicRanges * 1.30.1 2017-12-21
## GetoptLong 0.1.6 2017-03-07
## ggplot2 * 2.2.1 2016-12-30
## ggrepel 0.7.0 2017-09-29
## GlobalOptions 0.0.12 2017-05-21
## glue 1.2.0 2017-10-29
## graphics * 3.4.3 2017-12-07
## grDevices * 3.4.3 2017-12-07
## grid 3.4.3 2017-12-07
## gridExtra 2.3 2017-09-09
## grr 0.9.5 2016-08-26
## gtable 0.2.0 2016-02-26
## haven 1.1.1 2018-01-18
## highr 0.6 2016-05-09
## Hmisc 4.1-1 2018-01-03
## hms 0.4.1 2018-01-24
## htmlTable 1.11.2 2018-01-20
## htmltools 0.3.6 2017-04-28
## htmlwidgets 1.0 2018-01-20
## httpuv 1.3.5 2017-07-04
## httr 1.3.1 2017-08-20
## interactiveDisplayBase 1.16.0 2017-10-31
## IRanges * 2.12.0 2017-10-31
## jsonlite 1.5 2017-06-01
## knitr * 1.19 2018-01-29
## labeling 0.3 2014-08-23
## lattice 0.20-35 2017-03-25
## latticeExtra 0.6-28 2016-02-09
## lazyeval 0.2.1 2017-10-29
## limma 3.34.6 2018-01-24
## locfit 1.5-9.1 2013-04-20
## logging 0.7-103 2013-04-12
## lubridate 1.7.1 2017-11-03
## magrittr 1.5 2014-11-22
## Matrix 1.2-12 2017-11-20
## Matrix.utils 0.9.6 2017-08-28
## MatrixModels 0.4-1 2015-08-22
## matrixStats * 0.53.0 2018-01-24
## memoise 1.1.0 2017-04-21
## methods * 3.4.3 2017-12-07
## mime 0.5 2016-07-07
## mnormt 1.5-5 2016-10-15
## modelr 0.1.1 2017-07-24
## munsell 0.4.3 2016-02-13
## nlme 3.1-131 2017-02-06
## nnet 7.3-12 2016-02-02
## Nozzle.R1 1.1-1 2013-05-15
## parallel * 3.4.3 2017-12-07
## pheatmap 1.0.8 2015-12-11
## pillar 1.1.0 2018-01-14
## pkgconfig 2.0.1 2017-03-21
## plyr 1.8.4 2016-06-08
## preprocessCore 1.40.0 2017-10-31
## prettyunits 1.0.2 2015-07-13
## progress 1.1.2 2016-12-14
## ProtGenerics 1.10.0 2017-10-31
## psych 1.7.8 2017-09-09
## purrr * 0.2.4 2017-10-18
## quantreg * 5.34 2017-10-25
## R.methodsS3 1.7.1 2016-02-16
## R.oo 1.21.0 2016-11-01
## R.utils 2.6.0 2017-11-05
## R6 2.2.2 2017-06-17
## RColorBrewer 1.1-2 2014-12-07
## Rcpp 0.12.15 2018-01-20
## RCurl 1.95-4.10 2018-01-04
## readr * 1.1.1 2017-05-16
## readxl 1.0.0 2017-04-18
## reshape 0.8.7 2017-08-06
## reshape2 1.4.3 2017-12-11
## rjson 0.2.15 2014-11-03
## rlang 0.1.6 2017-12-21
## rmarkdown 1.8 2017-11-17
## RMySQL 0.10.13 2017-08-14
## rpart 4.1-12 2018-01-12
## rprojroot 1.3-2 2018-01-03
## Rsamtools 1.30.0 2017-10-31
## RSQLite 2.0 2017-06-19
## rstudioapi 0.7 2017-09-07
## rtracklayer 1.38.3 2018-01-23
## rvest 0.3.2 2016-06-17
## S4Vectors * 0.16.0 2017-10-31
## scales 0.5.0 2017-08-24
## shape 1.4.3 2017-08-16
## shiny 1.0.5 2017-08-23
## SparseM * 1.77 2017-04-23
## splines 3.4.3 2017-12-07
## stats * 3.4.3 2017-12-07
## stats4 * 3.4.3 2017-12-07
## stringi 1.1.6 2017-11-17
## stringr * 1.2.0 2017-02-18
## SummarizedExperiment * 1.8.1 2017-12-19
## survival 2.41-3 2017-04-04
## tibble * 1.4.2 2018-01-22
## tidyr * 0.8.0 2018-01-29
## tidyverse * 1.2.1 2017-11-14
## tools 3.4.3 2017-12-07
## tximport 1.6.0 2017-10-31
## utils * 3.4.3 2017-12-07
## viridis 0.4.1 2018-01-08
## viridisLite 0.3.0 2018-02-01
## vsn 3.46.0 2017-10-31
## withr 2.1.1 2017-12-19
## XML 3.98-1.9 2017-06-19
## xml2 1.2.0 2018-01-24
## xtable 1.8-2 2016-02-05
## XVector 0.18.0 2017-10-31
## yaml 2.1.16 2017-12-12
## zlibbioc 1.24.0 2017-10-31
## source
## cran (@1.4.1)
## cran (@1.56.0)
## cran (@1.48.0)
## Bioconductor
## Bioconductor
## cran (@1.2.0)
## cran (@2.10.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## local
## CRAN (R 3.4.0)
## Github (steinbaugh/basejump@265d3ce)
## Github (hbc/bcbioBase@dc61e83)
## Github (hbc/bcbioRNASeq@f44ad74)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## Bioconductor
## Bioconductor
## Bioconductor
## cran (@1.12.0)
## cran (@2.34.2)
## cran (@2.46.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@1.8.5)
## cran (@0.4.3)
## CRAN (R 3.4.2)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## local
## cran (@1.17.1)
## cran (@1.42.0)
## cran (@0.9.2)
## CRAN (R 3.4.1)
## CRAN (R 3.4.3)
## cran (@1.10.4-)
## local
## CRAN (R 3.4.0)
## cran (@1.14.1)
## cran (@0.4.1)
## cran (@0.3.3)
## cran (@1.18.1)
## CRAN (R 3.4.2)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@3.20.7)
## cran (@2.2.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@1.5)
## cran (@1.2-2)
## Bioconductor
## Bioconductor
## cran (@1.14.0)
## Bioconductor
## cran (@1.14.1)
## Bioconductor
## cran (@1.30.1)
## cran (@0.1.6)
## CRAN (R 3.4.0)
## cran (@0.7.0)
## cran (@0.0.12)
## CRAN (R 3.4.2)
## local
## local
## local
## cran (@2.3)
## cran (@0.9.5)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@4.1-1)
## CRAN (R 3.4.3)
## cran (@1.11.2)
## CRAN (R 3.4.0)
## cran (@1.0)
## cran (@1.3.5)
## CRAN (R 3.4.1)
## cran (@1.16.0)
## Bioconductor
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@0.6-28)
## CRAN (R 3.4.2)
## cran (@3.34.6)
## CRAN (R 3.4.0)
## cran (@0.7-103)
## CRAN (R 3.4.2)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## cran (@0.9.6)
## cran (@0.4-1)
## cran (@0.53.0)
## CRAN (R 3.4.0)
## local
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## cran (@1.1-1)
## local
## cran (@1.0.8)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## cran (@1.40.0)
## cran (@1.0.2)
## cran (@1.1.2)
## cran (@1.10.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@5.34)
## cran (@1.7.1)
## cran (@1.21.0)
## cran (@2.6.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## CRAN (R 3.4.0)
## cran (@0.8.7)
## CRAN (R 3.4.3)
## cran (@0.2.15)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## cran (@0.10.13)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## cran (@1.30.0)
## CRAN (R 3.4.1)
## CRAN (R 3.4.1)
## cran (@1.38.3)
## CRAN (R 3.4.0)
## Bioconductor
## CRAN (R 3.4.1)
## cran (@1.4.3)
## cran (@1.0.5)
## cran (@1.77)
## local
## local
## local
## CRAN (R 3.4.2)
## CRAN (R 3.4.0)
## cran (@1.8.1)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.3)
## CRAN (R 3.4.2)
## local
## cran (@1.6.0)
## local
## cran (@0.4.1)
## CRAN (R 3.4.3)
## cran (@3.46.0)
## CRAN (R 3.4.3)
## CRAN (R 3.4.1)
## CRAN (R 3.4.3)
## CRAN (R 3.4.0)
## cran (@0.18.0)
## CRAN (R 3.4.3)
## cran (@1.24.0)> mdHeader("`utils::sessionInfo()`", level = 2)utils::sessionInfo()
> sessionInfo()## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2 forcats_0.2.0
## [3] stringr_1.2.0 dplyr_0.7.4
## [5] purrr_0.2.4 readr_1.1.1
## [7] tidyr_0.8.0 tibble_1.4.2
## [9] ggplot2_2.2.1 tidyverse_1.2.1
## [11] knitr_1.19 bcbioRNASeq_0.1.4
## [13] DEGreport_1.14.1 quantreg_5.34
## [15] SparseM_1.77 bcbioBase_0.0.3
## [17] DESeq2_1.18.1 SummarizedExperiment_1.8.1
## [19] DelayedArray_0.4.1 matrixStats_0.53.0
## [21] Biobase_2.38.0 GenomicRanges_1.30.1
## [23] GenomeInfoDb_1.14.0 IRanges_2.12.0
## [25] S4Vectors_0.16.0 BiocGenerics_0.24.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.0.0 backports_1.1.2
## [3] circlize_0.4.3 Hmisc_4.1-1
## [5] AnnotationHub_2.10.1 plyr_1.8.4
## [7] ConsensusClusterPlus_1.42.0 lazyeval_0.2.1
## [9] splines_3.4.3 BiocParallel_1.12.0
## [11] digest_0.6.15 BiocInstaller_1.28.0
## [13] ensembldb_2.2.0 htmltools_0.3.6
## [15] viridis_0.4.1 magrittr_1.5
## [17] checkmate_1.8.5 memoise_1.1.0
## [19] cluster_2.0.6 limma_3.34.6
## [21] ComplexHeatmap_1.17.1 Biostrings_2.46.0
## [23] annotate_1.56.1 Nozzle.R1_1.1-1
## [25] modelr_0.1.1 R.utils_2.6.0
## [27] prettyunits_1.0.2 colorspace_1.3-2
## [29] rvest_0.3.2 blob_1.1.0
## [31] ggrepel_0.7.0 haven_1.1.1
## [33] crayon_1.3.4 jsonlite_1.5
## [35] tximport_1.6.0 RCurl_1.95-4.10
## [37] genefilter_1.60.0 bindr_0.1
## [39] survival_2.41-3 glue_1.2.0
## [41] gtable_0.2.0 zlibbioc_1.24.0
## [43] XVector_0.18.0 MatrixModels_0.4-1
## [45] GetoptLong_0.1.6 shape_1.4.3
## [47] scales_0.5.0 vsn_3.46.0
## [49] pheatmap_1.0.8 DBI_0.7
## [51] edgeR_3.20.7 Rcpp_0.12.15
## [53] viridisLite_0.3.0 xtable_1.8-2
## [55] progress_1.1.2 htmlTable_1.11.2
## [57] foreign_0.8-69 bit_1.1-12
## [59] preprocessCore_1.40.0 Formula_1.2-2
## [61] htmlwidgets_1.0 httr_1.3.1
## [63] RColorBrewer_1.1-2 acepack_1.4.1
## [65] reshape_0.8.7 pkgconfig_2.0.1
## [67] XML_3.98-1.9 R.methodsS3_1.7.1
## [69] nnet_7.3-12 locfit_1.5-9.1
## [71] labeling_0.3 reshape2_1.4.3
## [73] rlang_0.1.6 AnnotationDbi_1.40.0
## [75] munsell_0.4.3 cellranger_1.1.0
## [77] tools_3.4.3 cli_1.0.0
## [79] RSQLite_2.0 devtools_1.13.4
## [81] broom_0.4.3 evaluate_0.10.1
## [83] yaml_2.1.16 bit64_0.9-7
## [85] AnnotationFilter_1.2.0 nlme_3.1-131
## [87] mime_0.5 formatR_1.5
## [89] R.oo_1.21.0 grr_0.9.5
## [91] xml2_1.2.0 biomaRt_2.34.2
## [93] compiler_3.4.3 rstudioapi_0.7
## [95] curl_3.1 interactiveDisplayBase_1.16.0
## [97] affyio_1.48.0 geneplotter_1.56.0
## [99] stringi_1.1.6 highr_0.6
## [101] GenomicFeatures_1.30.2 lattice_0.20-35
## [103] ProtGenerics_1.10.0 Matrix_1.2-12
## [105] psych_1.7.8 pillar_1.1.0
## [107] GlobalOptions_0.0.12 data.table_1.10.4-3
## [109] cowplot_0.9.2 bitops_1.0-6
## [111] Matrix.utils_0.9.6 httpuv_1.3.5
## [113] rtracklayer_1.38.3 affy_1.56.0
## [115] R6_2.2.2 latticeExtra_0.6-28
## [117] RMySQL_0.10.13 gridExtra_2.3
## [119] codetools_0.2-15 assertthat_0.2.0
## [121] rprojroot_1.3-2 rjson_0.2.15
## [123] withr_2.1.1 mnormt_1.5-5
## [125] GenomicAlignments_1.14.1 Rsamtools_1.30.0
## [127] GenomeInfoDbData_1.0.0 hms_0.4.1
## [129] grid_3.4.3 rpart_4.1-12
## [131] rmarkdown_1.8 dendsort_0.3.3
## [133] logging_0.7-103 lubridate_1.7.1
## [135] shiny_1.0.5 base64enc_0.1-3
## [137] basejump_0.2.0> mdHeader("YAML params", level = 2)YAML params
> print(params)## $bcbFile
## [1] "data/bcb_sub.rda"
##
## $design
## ~sampleclass
## <environment: 0x7fc00df2d708>
##
## $contrast
## [1] "sampleclass" "primary_tumor" "lung_notumor"
##
## $alpha
## [1] 0.05
##
## $lfc
## [1] 1
##
## $outputDir
## [1] "."Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1). [Royal Statistical Society, Wiley]: 289–300. http://www.jstor.org/stable/2346101.
Cui, Xiangqin, and Gary A Churchill. 2003. “Statistical Tests for Differential Expression in cDNA Microarray Experiments.” Genome Biol. 4 (4): 210. https://www.ncbi.nlm.nih.gov/pubmed/12702200.
Kolde, Raivo. 2015. Pheatmap: Pretty Heatmaps. https://CRAN.R-project.org/package=pheatmap.
Li, Wentian, Jan Freudenberg, Young Ju Suh, and Yaning Yang. 2014. “Using Volcano Plots and Regularized-Chi Statistics in Genetic Association Studies.” Comput. Biol. Chem. 48 (February): 77–83. doi:10.1016/j.compbiolchem.2013.02.003.
Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12): 550. doi:10.1186/s13059-014-0550-8.
Patro, Rob, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nat. Methods 14 (4): 417–19. doi:10.1038/nmeth.4197.
Soneson, Charlotte, Michael I Love, and Mark D Robinson. 2016. “Differential Analyses for RNA-seq: Transcript-Level Estimates Improve Gene-Level Inferences.” F1000Res. 4 (December). doi:10.12688/f1000research.7563.1.
Ward, Joe H, Jr. 1963. “Hierarchical Grouping to Optimize an Objective Function.” Journal of the American Statistical Association 58 (301). Taylor & Francis: 236–44. doi:10.1080/01621459.1963.10500845.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Use R. Springer New York. doi:10.1007/978-0-387-98141-3.
Yang, Yee Hwa, Sandrine Dudoit, Percy Luu, David M Lin, Vivian Peng, John Ngai, and Terence P Speed. 2002. “Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation.” Nucleic Acids Res. 30 (4): e15. https://www.ncbi.nlm.nih.gov/pubmed/11842121.